Express Learning Course

Data and ML systems can continue to run normally even while an attacker poisons data, abuses an inference endpoint, or steals sensitive information. Monitoring only for uptime and failed jobs leaves those attacks invisible. This course teaches security-focused monitoring that examines data content, access patterns, and changes measured against a baseline.

Who Is It For

Data engineers, ML and MLOps engineers, platform engineers, and security practitioners who need to catch attacks on data and ML systems that standard monitoring misses.

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What You’ll Learn

Spot schema violations, implausible values, and data anomalies. Turn them into structured alerts, prioritize by severity, and choose the right response.

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What It Prepares You For

Add security monitoring to the data pipelines and ML systems you support, and take an effective role in the early stages of incident response on AI-enabled teams.

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Course Outline

Chapter 1. Course Introduction

Chapter 2. Recognizing Security Anomalies in Data Systems

Chapter 3. Logging and Measuring Security Signals

Chapter 4. Turning Security Signals into Actionable Alerts

Chapter 5. Responding to Data and ML Security Incidents

Chapter 6. Capstone & Conclusion

Prerequisites

Learners should understand the basics of data pipelines, including ingestion, validation, transformation, and downstream processing, and be familiar with common machine learning concepts such as training data, features, inference endpoints, and retraining. Comfort with application logs, metrics, and the command line is expected, along with the ability to read and make small edits to introductory Python and JSON. Advanced Python is not required. Prior experience with Prometheus, Alertmanager, Airflow, or incident response is helpful but not necessary.